Fix indentation and add requirements.txt for convenience

README.md

@@ -6,15 +6,14 @@ A Tensorflow implementation of [Unpaired Image-to-Image Translation using Cycle-
 Requirements:
 
 - Tensorflow 1.11
-- Python 3.6
 
 Thanks to the original authors PyTorch implementation for inspiration: https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix
 
 Project Organization
 ------------
 
  ├── README.md
- ├── Makefile  <- Makefile with commands like `make data` or `make train`
+ ├── requirements.txt <- Use `pip install -r requirements.txt`
  ├── setup.py <- makes project pip installable (pip install -e .) so src can be imported
  ├── LICENSE
  └── src <- Source code for use in this project

requirements.txt

@@ -0,0 +1,2 @@
+tensorflow==1.11.0
+numpy==1.16.2

src/train.py

@@ -27,26 +27,26 @@
  summary_writer = tf.contrib.summary.create_file_writer(log_dir, flush_millis=10000)
  for epoch in range(1, opt.epochs):
  start = time.time()
-  for train_step in range(batches_per_epoch):
-  # Record summaries every 100 train_steps, we multiply by 3 because there are 3 gradient updates per step.
-  with summary_writer.as_default(),
-  tf.contrib.summary.record_summaries_every_n_global_steps(opt.summary_freq * 3, global_step=global_step):
-  model.set_input(dataset.data)
-  model.optimize_parameters()
-  if opt.save_summaries:
-  # Summaries for Tensorboard:
-  tf.contrib.summary.scalar('loss/genA2B', model.genA2B_loss)
-  tf.contrib.summary.scalar('loss/genB2A', model.genB2A_loss)
-  tf.contrib.summary.scalar('loss/discA', model.discA_loss)
-  tf.contrib.summary.scalar('loss/discB', model.discB_loss)
-  tf.contrib.summary.scalar('loss/cyc', model.cyc_lossA + model.cyc_lossB)
-  tf.contrib.summary.scalar('loss/identity', model.id_lossA + model.id_lossB)
-  tf.contrib.summary.scalar('learning_rate', model.learning_rate)
-  tf.contrib.summary.image('A/generated', model.fakeA)
-  tf.contrib.summary.image('A/reconstructed', model.reconstructedA)
-  tf.contrib.summary.image('B/generated', model.fakeB)
-  tf.contrib.summary.image('B/reconstructed', model.reconstructedB)
-  print("Iteration complete")
+ for train_step in range(batches_per_epoch):
+ # Record summaries every 100 train_steps, we multiply by 3 because there are 3 gradient updates per step.
+ with summary_writer.as_default(), \
+ tf.contrib.summary.record_summaries_every_n_global_steps(opt.summary_freq * 3, global_step=global_step):
+ model.set_input(dataset.data)
+ model.optimize_parameters()
+ if opt.save_summaries:
+ # Summaries for Tensorboard:
+ tf.contrib.summary.scalar('loss/genA2B', model.genA2B_loss)
+ tf.contrib.summary.scalar('loss/genB2A', model.genB2A_loss)
+ tf.contrib.summary.scalar('loss/discA', model.discA_loss)
+ tf.contrib.summary.scalar('loss/discB', model.discB_loss)
+ tf.contrib.summary.scalar('loss/cyc', model.cyc_lossA + model.cyc_lossB)
+ tf.contrib.summary.scalar('loss/identity', model.id_lossA + model.id_lossB)
+ tf.contrib.summary.scalar('learning_rate', model.learning_rate)
+ tf.contrib.summary.image('A/generated', model.fakeA)
+ tf.contrib.summary.image('A/reconstructed', model.reconstructedA)
+ tf.contrib.summary.image('B/generated', model.fakeB)
+ tf.contrib.summary.image('B/reconstructed', model.reconstructedB)
+ print("Iteration complete")
  # Assign decayed learning rate:
  model.update_learning_rate(batches_per_epoch)
  # Checkpoint the model: